Nonlinear regression for description of strawberry (Fragaria x ananassa) production

The aim of this study was to select growth models to describe strawberry (Fragaria x ananassa)fruit production. To do this, data on 16 treatments (combination of 2 cultivars [Albion and Camarosa], 2 origins [National and Imported], and 4 organic substrates mixed [70% crushed sugar cane residue + 30% organic compost, 70% crushed sugar cane residue + 30% commercial substrate, 70% burnt rice husk + 30% organic compost, and 70% burnt rice husk + 30% commercial substrate]) conducted in a Randomized Complete Block Design (RCBD) with 4 replicates were used. Different parameterizations of the Logistic, Gompertz, and von Bertalanffy models were adjusted for number and weight of fruits per plant (g) accumulated in multiple harvests. The model adjustment and parameter estimation were obtained by ordinary least squares, using a Gauss Newton algorithm. The selection of the best model was chosen by intrinsic and parametric nonlinearity. Among the adjusted nonlinear models, the best adjustment for both variables was achieved by the parameterization 2 of the Logistic model and parameterization 1 of the Gompertz model, because they had lower results with less parametric and intrinsic nonlinearity. However, care should be taken when using the Gompertz model because it tends to overestimate the production estimate and may cause misunderstandings in interpretation.